More than Words: Syntactic Packaging and Implicit Sentiment
24019Authors
nan
Abstract
Work on sentiment analysis often focuses on the words and phrases that people use in overtly opinionated text. In this paper, we in- troduce a new approach to the problem that focuses not on lexical indicators, but on the syntactic “packaging” of ideas, which is well suited to investigating the identification of im- plicit sentiment, or perspective. We establish a strong predictive connection between linguis- tically well motivated features and implicit sentiment, and then show how computational approximations of these features can be used to improve on existing state-of-the-art senti- ment classification results. 1
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Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the ACL, pages 503–511, Boulder, Colorado, June 2009. c⃝2009 Association for Computational Linguistics More than Words: Syntactic Packaging and Implicit Sentiment Stephan Greene∗ ATG, Inc. 1111 19th St, NW Suite 600 Washington, DC 20036 sgreene@atg.com Philip Resnik Linguistics / UMIACS CLIP Laboratory University of Maryland College Park, MD 20742 resnik@umiacs.umd.edu Abstract Work on sentiment analysis often focuses on the words and phrases that people use in overtly opinionated text. In this paper, we in- troduce a new approach to the problem that focuses not on lexical indicators, but on the syntactic “packaging” of ideas, which is well suited to investigating the identification of im- plicit sentiment, or perspective. We establish a strong predictive connection between linguis- tically well motivated features and implicit sentiment, and then show how computational approximations of these features can be used to improve on existing state-of-the-art senti- ment classification results. 1 Introduction As Pang and Lee (2008) observe, the last several years have seen a “land rush” in research on senti- ment analysis and opinion mining, with a frequent emphasis on the identification of opinions in evalua- tive text such as movie or product reviews. How- ever, sentiment also may be carried implicitly by statements that are not only non-evaluative, but not even visibly subjective. Consider, for example, the following two descriptions of the same (invented) event: 1(a) On November 25, a soldier veered his jeep into a crowded market and killed three civilians. (b) On November 25, a soldier’s jeep veered into a crowded market, causing three civilian deaths. ∗This work was done while the first author was a student in the Department of Linguistics, University of Maryland. Both descriptions appear on the surface to be objec- tive statements, and they use nearly the same words. Lexically, the sentences’ first clauses differ only in the difference between ’s and his to express the rela- tionship between the soldier and the jeep, and in the second clauses both kill and death are terms with negative connotations, at least according to the Gen- eral Inquirer lexicon (Stone, 1966). Yet the descrip- tions clearly differ in the feelings they evoke: if the soldier were being tried for his role in what hap- pened on November 25, surely the prosecutor would be more likely to say (1a) to the jury, and the defense attorney (1b), rather than the reverse.1 Why, then, should a description like (1a) be per- ceived as less sympathetic to the soldier than (1b)? If the difference is not in the words, it must be in the way they are put together; that is, the structure of the sentence. In Section 2, we offer a specific hy- pothesis about the connection between structure and implicit sentiment: we suggest that the relationship is mediated by a set of “grammatically relevant” se- mantic properties well known to be important cross- linguistically in characterizing the interface between syntax and lexical semantics. In Section 3, we val- …
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Labeled Text
Work on sentiment analysis often focuses on the words and phrases that people use in overtly opinionated text. In this paper, we introduce a new approach to the problem that focuses not on lexical indicators, but on the syntactic “packaging” of ideas, which is well suited to investigating the identification of implicit sentiment, or perspective. We establish a strong predictive connection between linguistically well motivated features and implicit sentiment, and then show how computational approximations of these features can be used to improve on existing state-of-the-art sentiment classification results. 1
Publication Details
- Paper ID:
- 24019
- Year:
- 2009
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan